Dept Tea: Classification of Hand Motor Imagery Using an Affordable BCI System

Abstract: Multiple past studies has shown that motor imagery of the opening and closing of the hand can be effectively categorized with high accuracy using laboratory grade BCI systems. In our study we sought out to determine if a relatively affordable BCI setup can also classify the same sets of motor imagery with acceptable accuracy rates. To accomplish this, we first implemented our own BCI system in Java using an OpenBCI EEG headset as the signal acquisition device and a 3D printed robotic arm as the feedback interface. With our system in place, we ran multiple trials to record separate EEG data associated with the motor imagery of the closing and opening of the right hand. Using the LIBSVM package, we were able to train a set of models based on the pre-recorded trials which we then used to tests against random sample data to determine classification accuracy. By testing subsets of pre-recorded trial data on the model, we received prediction accuracy of over 80%.